Data Transmission Method, Data Transmission Apparatus and Program

3. The data transmission method according to claim 1, wherein the evaluating of the relation includes obtaining a distance between attribute information of the first data and attribute information of the second data with respect to identical attribute information and obtaining a sum of obtained distances when there are a plurality of pieces of attribute information.

4. The data transmission method according to claim 1, wherein the evaluating of the relation includes classifying the first data into classes by a clustering method using attribute information, obtaining a value of a probability of the second data being classified into classes, and determining that the relation is high when the first data is classified into a class having a maximum probability value.

5. The data transmission method according to claim 1, wherein the evaluating of the relation includes obtaining a correlation coefficient between a data set having an identical transmission source to the first data and a data set having an identical transmission source to the second data.

8. The data transmission apparatus according to claim 6, wherein the evaluating of the relation includes obtaining a distance between attribute information of the first data and attribute information of the second data with respect to identical attribute information and obtaining a sum of obtained distances when there are a plurality of pieces of attribute information.

9. The data transmission apparatus according to claim 6, wherein the evaluating of the relation includes classifying the first data into classes by a clustering method using attribute information, obtaining a value of a probability of the second data being classified into classes, and determining that the relation is high when the first data is classified into a class having a maximum probability value.

10. The data transmission apparatus according to claim 6, wherein the evaluating of the relation includes obtaining a correlation coefficient between a data set having an identical transmission source to the first data and a data set having an identical transmission source to the second data.

13. The non-transitory computer readable medium according to claim 11, wherein the evaluating of the relation includes obtaining a distance between attribute information of the first data and attribute information of the second data with respect to identical attribute information and obtaining a sum of obtained distances when there are a plurality of pieces of attribute information.

14. The non-transitory computer readable medium according to claim 11, wherein the evaluating of the relation includes classifying the first data into classes by a clustering method using attribute information, obtaining a value of a probability of the second data being classified into classes, and determining that the relation is high when the first data is classified into a class having a maximum probability value.

15. The non-transitory computer readable medium according to claim 11, wherein the evaluating of the relation includes obtaining a correlation coefficient between a data set having an identical transmission source to the first data and a data set having an identical transmission source to the second data.